Started by timer Running as SYSTEM Building in workspace /var/lib/jenkins/jobs/tensorflow/workspace [SSH] script: module load anaconda3_gpu module load cudnn cd python rm -f tensorflow_test.txt srun --nodes=1 --ntasks-per-node=1 --mem=16g \ --time=00:05:00 \ --partition=a100rome \ --account=bbmb-hydro \ --gpus-per-node=1 \ --gpu-bind=closest \ python3 cifar10gpu.py | tee tensorflow_test.txt YVALUE=`grep "wall time seconds" tensorflow_test.txt | cut -d'=' -f2` echo YVALUE=$YVALUE > tensorflow_time.txt [SSH] executing... srun: job 97880 queued and waiting for resources srun: job 97880 has been allocated resources 2024-11-18 07:43:02.610527: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-11-18 07:43:02.747171: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 2024-11-18 07:43:03.720584: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /sw/spack/hydrogpu-2022-06/apps/cudnn/8.4.0.27-11.6-gcc-11.3.0-qbhsfav/lib:/sw/spack/hydrogpu-2022-06/apps/cuda/11.7.0-gcc-11.3.0-3ysno6b/lib64:/sw/spack/hydrogpu-2022-06/apps/openmpi/4.1.4-gcc-11.3.0-i6koipj/lib:/sw/spack/hydrogpu-2022-06/apps/gcc/11.3.0-gcc-8.4.1-kroofxa/lib64:/sw/spack/hydrogpu-2022-06/apps/gcc/11.3.0-gcc-8.4.1-kroofxa/lib 2024-11-18 07:43:03.720659: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /sw/spack/hydrogpu-2022-06/apps/cudnn/8.4.0.27-11.6-gcc-11.3.0-qbhsfav/lib:/sw/spack/hydrogpu-2022-06/apps/cuda/11.7.0-gcc-11.3.0-3ysno6b/lib64:/sw/spack/hydrogpu-2022-06/apps/openmpi/4.1.4-gcc-11.3.0-i6koipj/lib:/sw/spack/hydrogpu-2022-06/apps/gcc/11.3.0-gcc-8.4.1-kroofxa/lib64:/sw/spack/hydrogpu-2022-06/apps/gcc/11.3.0-gcc-8.4.1-kroofxa/lib 2024-11-18 07:43:03.720665: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. 2024-11-18 07:43:06.755414: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-11-18 07:43:07.118439: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 79148 MB memory: -> device: 0, name: NVIDIA A100 80GB PCIe, pci bus id: 0000:21:00.0, compute capability: 8.0 (50000, 32, 32, 3) (10000, 32, 32, 3) Epoch 1/10 2024-11-18 07:43:09.114163: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. 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2s 1ms/step - loss: 1.3902 - accuracy: 0.5108 Epoch 7/10 1/1563 [..............................] - ETA: 2s - loss: 1.4721 - accuracy: 0.4688 38/1563 [..............................] - ETA: 2s - loss: 1.3714 - accuracy: 0.5058 75/1563 [>.............................] - ETA: 2s - loss: 1.3547 - accuracy: 0.5188 112/1563 [=>............................] - ETA: 1s - loss: 1.3568 - accuracy: 0.5226 149/1563 [=>............................] - ETA: 1s - loss: 1.3523 - accuracy: 0.5252 186/1563 [==>...........................] - ETA: 1s - loss: 1.3634 - accuracy: 0.5208 223/1563 [===>..........................] - ETA: 1s - loss: 1.3484 - accuracy: 0.5261 260/1563 [===>..........................] - ETA: 1s - loss: 1.3477 - accuracy: 0.5260 297/1563 [====>.........................] - ETA: 1s - loss: 1.3524 - accuracy: 0.5250 334/1563 [=====>........................] - ETA: 1s - loss: 1.3531 - accuracy: 0.5245 371/1563 [======>.......................] - ETA: 1s - loss: 1.3526 - accuracy: 0.5264 408/1563 [======>.......................] - 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2s 1ms/step - loss: 1.2864 - accuracy: 0.5488 Epoch 10/10 1/1563 [..............................] - ETA: 2s - loss: 1.2214 - accuracy: 0.5625 38/1563 [..............................] - ETA: 2s - loss: 1.1907 - accuracy: 0.5781 75/1563 [>.............................] - ETA: 2s - loss: 1.2245 - accuracy: 0.5700 113/1563 [=>............................] - ETA: 1s - loss: 1.2233 - accuracy: 0.5705 150/1563 [=>............................] - ETA: 1s - loss: 1.2342 - accuracy: 0.5696 187/1563 [==>...........................] - ETA: 1s - loss: 1.2438 - accuracy: 0.5667 224/1563 [===>..........................] - ETA: 1s - loss: 1.2463 - accuracy: 0.5636 261/1563 [====>.........................] - ETA: 1s - loss: 1.2409 - accuracy: 0.5637 298/1563 [====>.........................] - ETA: 1s - loss: 1.2379 - accuracy: 0.5649 335/1563 [=====>........................] - ETA: 1s - loss: 1.2406 - accuracy: 0.5624 372/1563 [======>.......................] - ETA: 1s - loss: 1.2412 - accuracy: 0.5629 409/1563 [======>.......................] - 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ETA: 0s - loss: 1.2503 - accuracy: 0.5601 1338/1563 [========================>.....] - ETA: 0s - loss: 1.2493 - accuracy: 0.5604 1375/1563 [=========================>....] - ETA: 0s - loss: 1.2490 - accuracy: 0.5608 1413/1563 [==========================>...] - ETA: 0s - loss: 1.2491 - accuracy: 0.5608 1451/1563 [==========================>...] - ETA: 0s - loss: 1.2485 - accuracy: 0.5611 1489/1563 [===========================>..] - ETA: 0s - loss: 1.2494 - accuracy: 0.5612 1526/1563 [============================>.] - ETA: 0s - loss: 1.2514 - accuracy: 0.5608 1563/1563 [==============================] - ETA: 0s - loss: 1.2517 - accuracy: 0.5609 1563/1563 [==============================] - 2s 1ms/step - loss: 1.2517 - accuracy: 0.5609 Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= flatten (Flatten) (None, 3072) 0 dense (Dense) (None, 3000) 9219000 dense_1 (Dense) (None, 1000) 3001000 dense_2 (Dense) (None, 10) 10010 ================================================================= Total params: 12,230,010 Trainable params: 12,230,010 Non-trainable params: 0 _________________________________________________________________ None wall time seconds (without loading test data set)= 24.155413389205933 hydro05 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins14891275507033155281.sh + scp 'HYDRO_REMOTE:~svchydrojenkins/python/tensorflow_time.txt' /var/lib/jenkins/jobs/tensorflow/workspace Recording plot data Saving plot series data from: /var/lib/jenkins/jobs/tensorflow/workspace/tensorflow_time.txt Finished: SUCCESS